/*
    * Licensed to the Apache Software Foundation (ASF) under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * The ASF licenses this file to You under the Apache License, Version 2.0
    * (the "License"); you may not use this file except in compliance with
    * the License.  You may obtain a copy of the License at
    *
    *      http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  package org.apache.commons.math3.stat.descriptive.moment;
  
  import java.io.Serializable;
  
  import org.apache.commons.math3.exception.MathIllegalArgumentException;
  import org.apache.commons.math3.exception.NullArgumentException;
  import org.apache.commons.math3.stat.descriptive.AbstractStorelessUnivariateStatistic;
  import org.apache.commons.math3.stat.descriptive.WeightedEvaluation;
  import org.apache.commons.math3.stat.descriptive.summary.Sum;
  import org.apache.commons.math3.util.MathUtils;
  
  /**
   * <p>Computes the arithmetic mean of a set of values. Uses the definitional
   * formula:</p>
   * <p>
   * mean = sum(x_i) / n
   * </p>
   * <p>where <code>n</code> is the number of observations.
   * </p>
   * <p>When {@link #increment(double)} is used to add data incrementally from a
   * stream of (unstored) values, the value of the statistic that
   * {@link #getResult()} returns is computed using the following recursive
   * updating algorithm: </p>
   * <ol>
   * <li>Initialize <code>m = </code> the first value</li>
   * <li>For each additional value, update using <br>
   *   <code>m = m + (new value - m) / (number of observations)</code></li>
   * </ol>
   * <p> If {@link #evaluate(double[])} is used to compute the mean of an array
   * of stored values, a two-pass, corrected algorithm is used, starting with
   * the definitional formula computed using the array of stored values and then
   * correcting this by adding the mean deviation of the data values from the
   * arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing
   * Sample Means and Variances," Robert F. Ling, Journal of the American
   * Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866. </p>
   * <p>
   *  Returns <code>Double.NaN</code> if the dataset is empty.
   * </p>
   * <strong>Note that this implementation is not synchronized.</strong> If
   * multiple threads access an instance of this class concurrently, and at least
   * one of the threads invokes the <code>increment()</code> or
   * <code>clear()</code> method, it must be synchronized externally.
   *
   * @version $Id: Mean.java 1416643 2012-12-03 19:37:14Z tn $
   */
  public class Mean extends AbstractStorelessUnivariateStatistic
      implements Serializable, WeightedEvaluation {
  
      /** Serializable version identifier */
      private static final long serialVersionUID = -1296043746617791564L;
  
      /** First moment on which this statistic is based. */
      protected FirstMoment moment;
  
      /**
       * Determines whether or not this statistic can be incremented or cleared.
       * <p>
       * Statistics based on (constructed from) external moments cannot
       * be incremented or cleared.</p>
       */
      protected boolean incMoment;
  
      /** Constructs a Mean. */
      public Mean() {
          incMoment = true;
          moment = new FirstMoment();
      }
  
      /**
       * Constructs a Mean with an External Moment.
       *
       * @param m1 the moment
       */
      public Mean(final FirstMoment m1) {
          this.moment = m1;
          incMoment = false;
      }
  
      /**
       * Copy constructor, creates a new {@code Mean} identical
       * to the {@code original}
       *
       * @param original the {@code Mean} instance to copy
      * @throws NullArgumentException if original is null
      */
     public Mean(Mean original) throws NullArgumentException {
         copy(original, this);
     }
 
     /**
      * {@inheritDoc}
      * <p>Note that when {@link #Mean(FirstMoment)} is used to
      * create a Mean, this method does nothing. In that case, the
      * FirstMoment should be incremented directly.</p>
      */
     @Override
     public void increment(final double d) {
         if (incMoment) {
             moment.increment(d);
         }
     }
 
     /**
      * {@inheritDoc}
      */
     @Override
     public void clear() {
         if (incMoment) {
             moment.clear();
         }
     }
 
     /**
      * {@inheritDoc}
      */
     @Override
     public double getResult() {
         return moment.m1;
     }
 
     /**
      * {@inheritDoc}
      */
     public long getN() {
         return moment.getN();
     }
 
     /**
      * Returns the arithmetic mean of the entries in the specified portion of
      * the input array, or <code>Double.NaN</code> if the designated subarray
      * is empty.
      * <p>
      * Throws <code>IllegalArgumentException</code> if the array is null.</p>
      * <p>
      * See {@link Mean} for details on the computing algorithm.</p>
      *
      * @param values the input array
      * @param begin index of the first array element to include
      * @param length the number of elements to include
      * @return the mean of the values or Double.NaN if length = 0
      * @throws MathIllegalArgumentException if the array is null or the array index
      *  parameters are not valid
      */
     @Override
     public double evaluate(final double[] values,final int begin, final int length)
     throws MathIllegalArgumentException {
         if (test(values, begin, length)) {
             Sum sum = new Sum();
             double sampleSize = length;
 
             // Compute initial estimate using definitional formula
             double xbar = sum.evaluate(values, begin, length) / sampleSize;
 
             // Compute correction factor in second pass
             double correction = 0;
             for (int i = begin; i < begin + length; i++) {
                 correction += values[i] - xbar;
             }
             return xbar + (correction/sampleSize);
         }
         return Double.NaN;
     }
 
     /**
      * Returns the weighted arithmetic mean of the entries in the specified portion of
      * the input array, or <code>Double.NaN</code> if the designated subarray
      * is empty.
      * <p>
      * Throws <code>IllegalArgumentException</code> if either array is null.</p>
      * <p>
      * See {@link Mean} for details on the computing algorithm. The two-pass algorithm
      * described above is used here, with weights applied in computing both the original
      * estimate and the correction factor.</p>
      * <p>
      * Throws <code>IllegalArgumentException</code> if any of the following are true:
      * <ul><li>the values array is null</li>
      *     <li>the weights array is null</li>
      *     <li>the weights array does not have the same length as the values array</li>
      *     <li>the weights array contains one or more infinite values</li>
      *     <li>the weights array contains one or more NaN values</li>
      *     <li>the weights array contains negative values</li>
      *     <li>the start and length arguments do not determine a valid array</li>
      * </ul></p>
      *
      * @param values the input array
      * @param weights the weights array
      * @param begin index of the first array element to include
      * @param length the number of elements to include
      * @return the mean of the values or Double.NaN if length = 0
      * @throws MathIllegalArgumentException if the parameters are not valid
      * @since 2.1
      */
     public double evaluate(final double[] values, final double[] weights,
                            final int begin, final int length) throws MathIllegalArgumentException {
         if (test(values, weights, begin, length)) {
             Sum sum = new Sum();
 
             // Compute initial estimate using definitional formula
             double sumw = sum.evaluate(weights,begin,length);
             double xbarw = sum.evaluate(values, weights, begin, length) / sumw;
 
             // Compute correction factor in second pass
             double correction = 0;
             for (int i = begin; i < begin + length; i++) {
                 correction += weights[i] * (values[i] - xbarw);
             }
             return xbarw + (correction/sumw);
         }
         return Double.NaN;
     }
 
     /**
      * Returns the weighted arithmetic mean of the entries in the input array.
      * <p>
      * Throws <code>MathIllegalArgumentException</code> if either array is null.</p>
      * <p>
      * See {@link Mean} for details on the computing algorithm. The two-pass algorithm
      * described above is used here, with weights applied in computing both the original
      * estimate and the correction factor.</p>
      * <p>
      * Throws <code>MathIllegalArgumentException</code> if any of the following are true:
      * <ul><li>the values array is null</li>
      *     <li>the weights array is null</li>
      *     <li>the weights array does not have the same length as the values array</li>
      *     <li>the weights array contains one or more infinite values</li>
      *     <li>the weights array contains one or more NaN values</li>
      *     <li>the weights array contains negative values</li>
      * </ul></p>
      *
      * @param values the input array
      * @param weights the weights array
      * @return the mean of the values or Double.NaN if length = 0
      * @throws MathIllegalArgumentException if the parameters are not valid
      * @since 2.1
      */
     public double evaluate(final double[] values, final double[] weights)
     throws MathIllegalArgumentException {
         return evaluate(values, weights, 0, values.length);
     }
 
     /**
      * {@inheritDoc}
      */
     @Override
     public Mean copy() {
         Mean result = new Mean();
         // No try-catch or advertised exception because args are guaranteed non-null
         copy(this, result);
         return result;
     }
 
 
     /**
      * Copies source to dest.
      * <p>Neither source nor dest can be null.</p>
      *
      * @param source Mean to copy
      * @param dest Mean to copy to
      * @throws NullArgumentException if either source or dest is null
      */
     public static void copy(Mean source, Mean dest)
         throws NullArgumentException {
         MathUtils.checkNotNull(source);
         MathUtils.checkNotNull(dest);
         dest.setData(source.getDataRef());
         dest.incMoment = source.incMoment;
         dest.moment = source.moment.copy();
     }
 }